Background/Objective: Hematoxylin and eosin (H&E) staining remains the routine reference in histopathology because it provides essential structural information for tissue evaluation. However, conventional H&E staining involves multiple reagent-based and operator-dependent procedures that may introduce variability in staining appearance. This study aimed to develop and preliminarily evaluate a technical proof-of-concept framework for generating H&E-like images from dual-modal autofluorescence microscopy data using a CycleGAN-based virtual staining approach. Methods: Human liver tissue sections were imaged using dual-modal autofluorescence, including DAPI-based nuclear fluorescence and endogenous tissue autofluorescence. A CycleGAN-based virtual staining framework with structural and perceptual constraints was developed to generate H&E-like images, followed by stain normalization to improve color consistency. The generated images were preliminarily evaluated by means of visual comparison, image-feature analysis, feature-space visualization, and nuclear counting. Results: The proposed framework generated H&E-like images with encouraging overall visual resemblance to conventional H&E images. Preliminary image-feature analysis suggested partial similarity between virtual and real H&E images, while feature-space visualization indicated that detectable differences remained. Nuclear counting on 168 images showed broadly consistent nuclear distribution between dual-modal autofluorescence and virtual H&E images, with minor discrepancies mainly related to thresholding artifacts. Some fine nuclear and chromatin-level details remained insufficiently reproduced in the current virtual H&E images. Conclusions: This study presents preliminary feasibility evidence for a technical proof-of-concept framework that translates dual-modal autofluorescence images into H&E-like images. In this small liver-tissue dataset, the generated images demonstrated encouraging overall H&E-like appearance and approximate nuclear localization. Further studies with larger and more diverse datasets, external validation, expert pathological assessment, diagnostic concordance analysis, and systematic workflow evaluation are warranted to assess robustness, diagnostic relevance, and potential utility in digital pathology workflows.

Precision measurement of aspherical mirrors is critical for high-end optical systems, where surface accuracy directly impacts performance. However, there remain challenges in this task: 1) measurement precision in a large scope; 2) larger ranges and flexibility in complex surface test; 3) environmental robustness in applications; and 4) efficiency and accuracy in data processing. This study introduces a novel multiwavelength point diffraction interferometry (MPDI) method, significantly enhancing measurement flexibility and accuracy. Through the diffraction, a high-accuracy wavefront ( lambda/10(-4)) is produced to perform relative measurement to aspherical mirrors. By integrating a triple-wavelength laser module and utilizing the beat frequency concept, the system generates at least nine equivalent wavelengths (245.8-3334.2 nm), enabling efficient testing of mirrors with both large surface deviations and high-resolution requirements. A key innovation is the random two-step phase retrieval method, which improves measurement efficiency by nearly 60% compared to conventional four-step methods, while minimizing the effects of environmental disturbances. The proposed system, equipped with machine vision alignment, ensures precise optical path alignment and enhances wavefront quality. Experimental results show a 5.3-fold increase in measurement range and rms errors as low as 0.6 x 10(-3 )lambda (lambda = 633 nm), with stitching-free testing. This MPDI method offers substantial improvements in measurement range, robustness, and efficiency, making it a valuable tool for precision aspherical mirror metrology.

For feature-based anomaly detection methods, the features extracted through previous texture-based data augmentation in self-supervised learning are inadequate for representing high-level semantic information. Therefore, they face challenges when detecting global, high-level semantic logical anomalies that violate certain fundamental logical or geometric constraints. In this study, we introduce a novel anomaly detection approach, SegLGAD. Specifically, we propose a novel data augmentation method based on SAM, the representations are learned by the designed self-supervised task. Subsequently, the local and global feature distributions of the training samples are established based on the embeddings extracted by the network acquired through selfsupervised learning. The anomaly scores for the test samples are comprehensively evaluated by calculating the distance between the embeddings of test samples and the local and global normal distributions established through self-supervised learning. We also provide a fiber optic jumper anomaly detection dataset based on real industrial needs, facilitating research on related issues and samples. Our approach achieves comparative results on three challenging industry anomaly detection datasets, confirming its effectiveness. The dataset is publicly available at the following link: MT-ribbon Optical Fiber Jumper Anomaly Detection Dataset.

At present, in the echo signals generated by ultrasonic non-destructive testing of carbon fiber composite materials, there inevitably exist a large number of structural noises, which cause great interference to the identification of defect signals and the detection of defects. To solve this problem, an improved U-Net-based denoising method is proposed to achieve effective noise suppression in ultrasonic signals. When applied to the echo signals of carbon fiber composite materials, the proposed method demonstrates superior noise reduction performance compared to both the conventional U-Net and wavelet denoising algorithms. The signal-to-noise ratio (SNR) is employed as the evaluation parameter for denoising effectiveness, and the results indicate that the proposed approach achieves a higher SNR, confirming its improved denoising capability over conventional U-Net and wavelet denoising algorithms. Furthermore, the denoised signals are stitched in a two-dimensional space to realize the planar reconstruction of defects. The reconstructed defect image is compared with the original un-denoised image and the image denoised by the traditional wavelet method, which demonstrates that proposed algorithm in this paper has stronger denoising effect and robustness.

Industrial defect segmentation is critical for manufacturing quality control. Due to the scarcity of training defect samples, few-shot semantic segmentation (FSS) holds significant value in this field. However, existing studies mostly apply FSS to tackle defects on simple textures, without considering more diverse scenarios. This paper aims to address this gap by exploring FSS in broader industrial products with various defect types. To this end, we contribute a new real-world dataset and reorganize some existing datasets to build a more comprehensive few-shot defect segmentation (FDS) benchmark. On this benchmark, we thoroughly investigate metric learning-based FSS methods, including those based on meta-learning and those based on Vision Foundation Models (VFM). We observe that existing meta-learning-based methods are generally not well-suited for this task, while VFMs hold great potential. We further systematically study two types of VFMs, including upstream representation learning models and downstream SAM (Segment anything) series models. We propose a novel training-free FDS method, called FM-SAM, which is based on feature matching combined with FastSAM for refinement. It demonstrates convincing segmentation performance while maintaining high efficiency. In addition, we find that SAM2 can directly perform effective FDS end-to-end through its video tracking mode. The contributed dataset and code are available at: https://github.com/liutongkun/GFDS.

With the advancement of metal additive manufacturing, components fabricated by this technique are being increasingly adopted across various industries. However, due to the influence of fabrication parameters, components tend to develop defects during the manufacturing process. Timely detection of defects in the heat-affected zone (the subsurface region) is crucial, as process parameters can be adjusted and reprocessing can be performed to eliminate such defects. As a non-contact, high-resolution technique, laser ultrasonic offers a powerful tool for defect detection in manufacturing processes. In this study, laser ultrasonic was employed to perform off-line detection of subsurface defects in additively manufactured specimens. Three sub-millimeter horizontal blind holes were fabricated at a depth of 1 mm below the surface of laser powder bed fusion manufactured TI-6AL-4 V specimens to simulate typical subsurface defects. The total focusing method was utilized for high-resolution planar imaging of the defects, while an optimized elliptical localization method was applied for accurate defect localization and sizing in the depth direction. By integrating the results from these two methods, 3-D mapping and reconstruction of the defects were achieved, enabling high-precision characterization of sub-surface defects.

Laser powder bed fusion (LPBF) is widely employed in metal additive manufacturing to fabricate components with outstanding mechanical properties and precise dimensions by melting powder layer-by-layer. As an in-line monitoring technique for additive manufacturing (AM), laser ultrasonic testing (LUT) is expected to be effective. During the LPBF process, ultrasonic signals are affected by thickness variations of specimens. This study analyzes the transmission of ultrasonic waves at different thicknesses and the variations in wave types. Realistic AM surface roughness data were incorporated into the simulation model to generate ultrasonic signals at various thicknesses. Subsequently, experimental studies were conducted. The research findings demonstrate that the Lamb wave characteristics are most prominent when the thickness is 0.2 mm. As the thickness increases, there is a gradual attenuation of the Lamb wave dispersion, accompanied by the emergence of surface wave features. The Lamb wave behavior diminishes as the thickness exceeds 3.021 mm, and surface wave, transverse wave, and longitudinal wave become more prominent. The dispersion curves were derived using the f-k method, and the thickness of LPBF Ti-6Al-4 V specimens smaller than 1 mm is precisely inversed based on dispersion curve. The verification experiments demonstrate that the model solution for thickness has a relative error of less than 5 %. Therefore, the proposed method overcomes the constraints of ultrasonic echo thickness measurement that cannot accurately measure thin specimens, while accomplishing non-contact evaluation based on laser-induced ultrasonic.

Additive manufactured (AM) metallic components have been increasingly applied across various industries. However, due to rapid temperature gradients and non-uniform thermal stresses during fabrication, defects such as cracks and pores are prone to occur. Laser ultrasonic testing faces challenges in achieving high-precision detection and imaging of surface defects on AM components, as strong scattering effects induced by surface roughness introduce considerable high-frequency noise and severely reduce the signal-to-noise ratio of defect echoes. To overcome this limitation, a combined VMD-TFM imaging method is proposed for the detection and characterization of submillimeter-scale surface defects on rough surfaces of AM Ti-6Al-4 V components. The approach introduces the Total Focusing Method (TFM)—originally developed for phased-array ultrasonic—into laser ultrasonic, where delay-compensated and coherently summed signals enable high-resolution, full-coverage imaging of the inspection area. In addition, Variational Mode Decomposition (VMD) is employed as a preprocessing step to denoise and reconstruct the effective signal modes, thereby suppressing high-frequency speckle and artifacts induced by surface roughness and enhancing the imaging precision for submillimeter defects. Validation through 3D surface metrology microscope shows that the spatial overlap between the VMD-TFM imaging and actual defect locations exceed 80 %, with the absolute diameter errors remain below 0.08 mm, confirming the high resolution and reliability of the proposed algorithm. This method provides a new pathway for accurate surface-quality evaluation and establishes a technical foundation for integrating laser ultrasonic testing into real-time monitoring and feedback control in additive manufacturing processes.

Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection. However, existing models usually suffer from the trade-off between normal reconstruction fidelity and abnormal reconstruction distinguishability, which damages the performance. In this paper, we find that the above trade-off can be better mitigated by leveraging the distinct frequency biases between normal and abnormal reconstruction errors. To this end, we propose Frequency-aware Image Restoration (FAIR), a novel self-supervised image restoration task that restores images from their high-frequency components. It enables precise reconstruction of normal patterns while mitigating unfavorable generalization to anomalies. Using only a simple vanilla UNet, FAIR achieves convincing performance with high efficiency on various industrial visual inspection datasets. Code: https://github.com/liutongkun/FAIR.

As a crucial component of air gauging sensors in intelligent CNC machine tools, air-electronic converters directly influence measurement accuracy. Nevertheless, conventional designs suffer from performance limitations due to undesirable dynamic/temperature characteristics in pressure conversion elements and accuracy loss during analog-to-digital (A/D) conversion. Aiming at this critical issue, a quasi-digital output micro-electromechanical system (MEMS) resonator-based air-electronic converter is presented and characterized in this article. The converter employs a gas conversion element to transform slot width/displacement into chamber pressure, utilizing a bi-chamber structure (feeding and measuring chambers) to suppress air supply fluctuations. A differential resonant pressure-sensing unit, incorporating two quartz double-ended tuning fork (DETF) resonators, is proposed to convert chamber pressure to frequency signals while compensating for temperature-induced errors. Characterization results demonstrate the linearity of <= 0.5 % full scale (FS), an actual resolution of 0.04 mu m, and enhanced stability through the combined output of four DETF resonators. An expanded uncertainty of 0.059 mu m in displacement measurements confirms the device's repeatability and reliability. This work advances the performance of air-electronic converters by enabling direct quasi-digital output and improved stability, offering a promising solution for high-precision air gauging sensors.

The evaluation of the film cooling performance of turbine blades necessitates the analysis of the geometric parameters of the cooling holes. Nevertheless, obtaining precise measurements poses a challenge due to the intricate structure of the cooling holes, especially the fan-shaped cooling holes with tiny diameters, to which conventional measurement methods cannot be applied. The paper presents a novel method for measuring the multi-view internal fusion morphology of cooling holes, aiming to evaluate geometric parameters accurately, which utilizes the optimal beluga whale optimization (OBWO) algorithm and a weighted variance algorithm that relies on the depth information of point clouds. Experiments simulating the cooling hole process are conducted to validate the proposed methodology. The findings indicate that the method effectively recovers the internal 3D shape of the cooling hole. The above method is also employed to quantify intricate fan-shaped cooling apertures on physical turbine blades. The cooling hole diameter exhibits a maximum error of less than 9.5 mu m, while the hole axis angle demonstrates a maximum deviation of less than 0.87. The proposed method accomplishes the measurement of the internal morphology of the cooling hole and evaluates its geometric parameters.

Microscopic images of surfaces can be used for non-contact roughness measurement by visual methods. However, the images are usually acquired manually and need to be as sharp as possible, which limits the general application of the method. This manuscript provides an automatic roughness measurement method that can apply to automatic industrial sites. This method first automatically acquires the sharpest image and then feeds the image into a convolutional neural network (CNN) model for roughness measurement. In this method, the weighted window enhanced sharpness evaluation algorithm based on the sharpness evaluation function is proposed to automatically extract the sharpest image. Then, a CNN model, CFEN, suitable for the roughness measurement task was designed and pre-trained. The results demonstrate that the measurement accuracy of the method reaches 91.25% and the time is within a few seconds. It is proved that the method has high accuracy and efficiency and is feasible in practical applications.

Additive manufacturing (AM), especially AM for metals, has become the most promising processing technique in recent years and is widely applied across several industries. Laser ultrasonic testing (LUT) has appeared as a potential method for identifying defects in additive manufactured components, but the rough surface of the additive manufactured component affects the ultrasonic signal. Analyzing the influence of surface roughness on ultrasonic signals and characterizing surface roughness through ultrasonic signals can improve the accuracy of defect detection and monitor the working conditions in the processing, which is a great significance for enhancing the quality of additive manufactured products. In this study, the influence of additive manufactured rough surface with different Ra on the ultrasonic signal has been analyzed. A novel simulation approach for the rough surface was provided, and the created surfaces with different Ra were applied to modeling. The results indicate that when the surface roughness increases, the ultrasonic signals suffer strong diffuse reflection, and the signal energy decreases. When the Ra is small, the surface of the specimen is close to smooth, the reflection of energy is enhanced, and part of the signals are reflected, so the signal energy is slightly lower. The ultrasonic signal of all models has been denoised and reconstructed based on the variational mode decomposition (VMD), and the analytical expression between the reconstructed signal energy and Ra was derived. The laser ultrasonic experiments were further designed for exciting signals from different Ra specimens, and the analytical expression relating reconstructed signals to Ra was also obtained, then the surface roughness of two specimens with unknown Ra was calculated by ultrasonic signals. A new method to obtain the roughness Ra of the additive manufactured components by ultrasonic signal is developed.

Existing unsupervised surface anomaly detection models have shown promising results. However, research on lightweight and fast anomaly detection models is relatively scarce, thereby impeding their practical application in industrial scenarios where rapid detection is required. Following the approach of reconstruction-based detection methods, we introduce EawT, an efficient anomaly detection model. EawT incorporates a novel pseudo-anomaly synthesis method and a depthwise separable convolutional structure to enhance network lightweighting. Additionally, considering that some studies indicate that Deep Neural Networks (DNNs) may exhibit preferences for certain frequency components during the learning process, potentially affecting the robustness of learned features, we propose an Adaptive Wavelet Transform module designed to modulate frequency components with transfer difficulties in latent space, thereby allowing for dynamic adjustment of feature information to adapt to various requirements. Furthermore, we propose wavelet loss to assist in the reconstruction of original images. Our method achieves comparable detection accuracy to existing reconstruction-based models on the VisA and BTAD datasets, with a frame rate of 228 FPS on a 3090Ti GPU and a lower parameter count, demonstrating the effectiveness of the proposed approach.

In ultrasonic testing, diffraction artifacts generated around defects increase the challenge of quantitatively characterizing defects. In this paper, we propose a label-enhanced semi-supervised CycleGAN network model, referred to as LESS-CycleGAN, which is a conditional cycle generative adversarial network designed for accurately characterizing defect morphology in ultrasonic testing images. The proposed method introduces paired cross-domain image samples during model training to achieve a defect transformation between the ultrasound image domain and the morphology image domain, thereby eliminating artifacts. Furthermore, the method incorporates a novel authenticity loss function to ensure high-precision defect reconstruction capability. To validate the effectiveness and robustness of the model, we use simulated 2D images of defects and corresponding ultrasonic detection images as training and test sets, and an actual ultrasonic phased array image of a test block as the validation set to evaluate the model's application performance. The experimental results demonstrate that the proposed method is convenient and effective, achieving subwavelength-scale defect reconstruction with good robustness.

SiCp/Al composites are suitable for satisfying the lightweight requirements of terahertz wave-reflecting components. In this paper, diamond-turned surfaces of 60 % high volume fraction SiCp/Al composites are characterized in terms of surface roughness and terahertz reflectivity. The finished surfaces of SiCp/Al composites are obtained using single-point diamond turning with different process parameters, and surface roughness measurements as well as SEM and EDS analyses are conducted on the surfaces. The theoretical relationship between surface roughness and terahertz reflectivity is analyzed, and terahertz reflectivity testing experiments are conducted and validated. The surface roughness evaluation system and prediction model for SiCp/Al composites with a volume fraction of 60 % are established, and the errors between the experimental results and the predicted results are verified within 10 % by random experiments. Finally, the turning process parameters are optimized using a genetic algorithm, and the optimized turning process parameters are obtained as the spindle speed of 750 r/min, the feeding speed of 1 mm/min and the turning depth of 6 mu m.

Selective laser melting (SLM) technology, employed in metal additive manufacturing, is widely utilised to fabricate components with precise dimensions and excellent mechanical properties by melting powder layer by layer. Laser ultrasonic testing is anticipated to be an efficient method for in-line monitoring in the additive manufacturing (AM) process. The conversion of specimens from thin to thick during the SLM manufacturing process has an impact on the ultrasonic signals. This paper focuses on the influence of AM specimen thickness changes on ultrasonic signals. In the finite element analysis, the incremental specimen thickness was maintained at 0.2 mm, which was the initial value. As the specimen thickness increased sequentially, ultrasonic signals were obtained from different specimens. The results demonstrate that Lamb waves are generated when the specimen is thin, and the dispersive characteristics weaken as the thickness increases. The dispersive phenomenon diminishes significantly once the specimen thickness exceeds 1 mm. By employing the f-k method, calculated dispersion curves for various specimen thicknesses were obtained. After fitting the derived dispersion curve, a mapping relationship is established with respect to the specimen thickness. Consequently, the fitting coefficient of the dispersion curve can therefore be utilized to characterize the thickness of the additive manufactured thin specimen.

Visual anomaly detection aims to identify areas where the appearance deviates from the normal distribution. Reconstruction-based methods detect anomalies through the analysis of reconstruction errors. In particular, Inpainting-based methods aim to reduce the potential for network generalization of anomalies by introducing random masks. However, larger anomalies may still be reconstructed, making them challenging to discriminate. Although enhancing the network's long-distance modeling capability offers the possibility to mitigate the reconstruction of anomalous regions, there is a possibility that the reconstruction error for normal regions increases and the ability to discriminate small anomalies is reduced due to the random nature of the mask placement. To address this issue, we introduce a correction branch to modify the original reconstruction results obtained from the reconstruction network during testing. This method can alleviate the potential increase in reconstruction errors in normal regions caused by the randomness of the mask to some extent and enhance the network's responsiveness to anomalous regions. We also propose a reconstruction network constructed with ConvNeXt blocks and the incorporation of the channel attention mechanism, which enhances the long-distance modeling ability, thereby improving the model's predictive capacity for large masked areas, this network also improves the reconstruction of textures, thereby improving the network's capacity to discriminate anomalies. The proposed method achieves competitive results on the challenging benchmark MVTec AD and BTAD datasets.

It is necessary to study the influence mechanism of blade thickness on the photoacoustic signals since material thickness will affect defect detection precision by affecting the types, velocity, and frequency of photoacoustic signals. In this article, the influence mechanism of blade thickness on sound waves is obtained by setting defects with different depths in different thicknesses areas of blades. This mechanism instructs the imaging detection and characterization of defects with different depths in different thicknesses. First, by combining with the dispersion curves, we found that there are more anti-symmetric $A_{0}$ modes in thin area. Second, different types of sound waves lead to different velocities of sound waves. The sound velocity in the thin area of the blades is 2747.58 m/s, and the sound velocity in the thick area is 2912.22 m/s. Further, compared with the thick area, the thin area has more peaks and more complex high-frequency components, but the frequency range is basically the same. Finally, the detection and characterization of blade defects are carried out by combining imaging inspection and influence mechanism. The research results can not only promote the basic research on the influence of thickness on photoacoustic signals, but also expand the application of laser ultrasound in blade defect detection.